{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-06T06:52:10.106578Z",
     "start_time": "2025-06-06T06:30:56.329405Z"
    }
   },
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import jieba\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from tqdm import tqdm\n",
    "from datetime import datetime\n",
    "\n",
    "# ------------------------------ 数据处理函数 ------------------------------\n",
    "def load_class_labels(class_path):\n",
    "    label_map = {}\n",
    "    if os.path.exists(class_path):\n",
    "        with open(class_path, 'r', encoding='utf-8') as f:\n",
    "            for idx, line in enumerate(f):\n",
    "                label_map[idx] = line.strip()\n",
    "    return label_map or {idx: f\"类别{idx}\" for idx in sorted(set(label_map.keys()))}\n",
    "\n",
    "def load_text_dataset(file_path):\n",
    "    texts, labels = [], []\n",
    "    with open(file_path, 'r', encoding='utf-8') as f:\n",
    "        for line in tqdm(f, desc=f\"加载 {file_path}\"):\n",
    "            line = line.strip()\n",
    "            if not line:\n",
    "                continue\n",
    "            parts = line.rsplit('\\t', 1)\n",
    "            if len(parts) == 2:\n",
    "                texts.append(parts[0])\n",
    "                labels.append(int(parts[1]))\n",
    "    return texts, labels\n",
    "\n",
    "def preprocess_texts(texts, stopwords):\n",
    "    processed = []\n",
    "    for text in tqdm(texts, desc=\"文本预处理\"):\n",
    "        words = jieba.cut(text, HMM=True)\n",
    "        filtered = [word for word in words if word not in stopwords and len(word) >= 2]\n",
    "        processed.append(' '.join(filtered))\n",
    "    return processed\n",
    "\n",
    "def load_imdb_data(data_path):\n",
    "    train_texts, train_labels = [], []\n",
    "    test_texts, test_labels = [], []\n",
    "\n",
    "    for label in ['pos', 'neg']:\n",
    "        for split in ['train', 'test']:\n",
    "            path = os.path.join(data_path, split, label)\n",
    "            for file_name in os.listdir(path):\n",
    "                if file_name.endswith('.txt'):\n",
    "                    with open(os.path.join(path, file_name), 'r', encoding='utf-8') as file:\n",
    "                        text = file.read()\n",
    "                        if split == 'train':\n",
    "                            train_texts.append(text)\n",
    "                            train_labels.append(1 if label == 'pos' else 0)\n",
    "                        else:\n",
    "                            test_texts.append(text)\n",
    "                            test_labels.append(1 if label == 'pos' else 0)\n",
    "    return train_texts, train_labels, test_texts, test_labels\n",
    "\n",
    "# ------------------------------ 模型训练函数 ------------------------------\n",
    "def run_svm(data, stopwords, label_map, max_iter=500):\n",
    "    train_texts, train_labels, test_texts, test_labels = data\n",
    "    classes = list(label_map.values())\n",
    "\n",
    "    # 特征提取\n",
    "    vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1, 2))\n",
    "    X_train = vectorizer.fit_transform(preprocess_texts(train_texts, stopwords))\n",
    "    X_test = vectorizer.transform(preprocess_texts(test_texts, stopwords))\n",
    "\n",
    "    # 初始化SVM模型，并设置最大迭代次数\n",
    "    model = SVC(kernel='linear', random_state=42, max_iter=max_iter)\n",
    "\n",
    "    # 训练模型\n",
    "    start_time = datetime.now()\n",
    "    model.fit(X_train, train_labels)\n",
    "    train_time = (datetime.now() - start_time).total_seconds()\n",
    "\n",
    "    # 预测\n",
    "    train_pred = model.predict(X_train)\n",
    "    test_pred = model.predict(X_test)\n",
    "    train_acc = accuracy_score(train_labels, train_pred)\n",
    "    test_acc = accuracy_score(test_labels, test_pred)\n",
    "\n",
    "    # 输出结果\n",
    "    print(f\"\\n---------------- SVM模型 (最大迭代次数: {max_iter}) ----------------\")\n",
    "    print(f\"训练集准确率: {train_acc:.4f}\")\n",
    "    print(f\"测试集准确率: {test_acc:.4f}\")\n",
    "    print(f\"训练时间: {train_time:.2f} 秒\")\n",
    "    print(\"分类报告:\\n\", classification_report(test_labels, test_pred, target_names=classes,digits=4))\n",
    "\n",
    "    return {\n",
    "        \"model\": \"SVM模型\",\n",
    "        \"test_accuracy\": test_acc,\n",
    "        \"train_time\": train_time,\n",
    "        \"confusion_matrix\": confusion_matrix(test_labels, test_pred)\n",
    "    }\n",
    "\n",
    "# ------------------------------ 主函数 ------------------------------\n",
    "def main():\n",
    "    print(f\"[{datetime.now()}] 文本分类实验开始...\")\n",
    "\n",
    "    # 新闻标题数据集\n",
    "    THUCNEWS_DATA_DIR = r\"D:\\Program Files\\JetBrains\\PyCharm 2024.2.1\\mytest\\pangchenchen\\sahgji-3\\THUCNews-txt\"\n",
    "    THUCNEWS_CLASS_PATH = os.path.join(THUCNEWS_DATA_DIR, \"class.txt\")\n",
    "    stopwords = {\n",
    "        '的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一',\n",
    "        '个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有'\n",
    "    }\n",
    "\n",
    "    # 加载新闻标题数据集\n",
    "    thucnews_train_texts, thucnews_train_labels = load_text_dataset(os.path.join(THUCNEWS_DATA_DIR, \"train.txt\"))\n",
    "    thucnews_test_texts, thucnews_test_labels = load_text_dataset(os.path.join(THUCNEWS_DATA_DIR, \"test.txt\"))\n",
    "    thucnews_label_map = load_class_labels(THUCNEWS_CLASS_PATH)\n",
    "    thucnews_data = (thucnews_train_texts, thucnews_train_labels, thucnews_test_texts, thucnews_test_labels)\n",
    "\n",
    "    # 输出新闻标题数据集信息\n",
    "    print(f\"新闻标题训练集样本数：{len(thucnews_train_texts)}\")\n",
    "    print(f\"新闻标题测试集样本数：{len(thucnews_test_texts)}\")\n",
    "\n",
    "    # 运行SVM模型 - 新闻标题数据集，设置最大迭代次数为100\n",
    "    thucnews_results = run_svm(thucnews_data, stopwords, thucnews_label_map, max_iter=500)\n",
    "\n",
    "    # 电影评论数据集\n",
    "    IMDB_DATA_DIR = r\"D:\\Program Files\\JetBrains\\PyCharm 2024.2.1\\mytest\\pangchenchen\\sahgji-3\\Movie\\aclImdb\"\n",
    "\n",
    "    # 加载电影评论数据集\n",
    "    imdb_train_texts, imdb_train_labels, imdb_test_texts, imdb_test_labels = load_imdb_data(IMDB_DATA_DIR)\n",
    "    imdb_label_map = {0: 'neg', 1: 'pos'}\n",
    "    imdb_data = (imdb_train_texts, imdb_train_labels, imdb_test_texts, imdb_test_labels)\n",
    "\n",
    "    # 输出电影评论数据集信息\n",
    "    print(f\"电影评论训练集样本数：{len(imdb_train_texts)}\")\n",
    "    print(f\"电影评论测试集样本数：{len(imdb_test_texts)}\")\n",
    "\n",
    "    # 运行SVM模型 - 电影评论数据集，设置最大迭代次数为100\n",
    "    imdb_results = run_svm(imdb_data, stopwords, imdb_label_map, max_iter=500)\n",
    "\n",
    "    return thucnews_results, imdb_results\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    thucnews_results, imdb_results = main()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2025-06-06 14:30:56.733426] 文本分类实验开始...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "加载 D:\\Program Files\\JetBrains\\PyCharm 2024.2.1\\mytest\\pangchenchen\\sahgji-3\\THUCNews-txt\\train.txt: 180000it [00:00, 1354768.80it/s]\n",
      "加载 D:\\Program Files\\JetBrains\\PyCharm 2024.2.1\\mytest\\pangchenchen\\sahgji-3\\THUCNews-txt\\test.txt: 10000it [00:00, 1267659.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "新闻标题训练集样本数：180000\n",
      "新闻标题测试集样本数：10000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理:   0%|          | 0/180000 [00:00<?, ?it/s]Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\Lenovo\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.522 seconds.\n",
      "Prefix dict has been built successfully.\n",
      "文本预处理: 100%|██████████| 180000/180000 [00:11<00:00, 16274.89it/s]\n",
      "文本预处理: 100%|██████████| 10000/10000 [00:01<00:00, 6140.27it/s]\n",
      "D:\\Program Files\\JetBrains\\PyCharm 2024.2.1\\test1\\venv\\Lib\\site-packages\\sklearn\\svm\\_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------- SVM模型 (最大迭代次数: 500) ----------------\n",
      "训练集准确率: 0.7629\n",
      "测试集准确率: 0.7495\n",
      "训练时间: 264.06 秒\n",
      "分类报告:\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "      finance     0.6958    0.8280    0.7562      1000\n",
      "       realty     0.8639    0.8190    0.8409      1000\n",
      "       stocks     0.6007    0.6560    0.6272      1000\n",
      "    education     0.8660    0.8980    0.8817      1000\n",
      "      science     0.6596    0.6550    0.6573      1000\n",
      "      society     0.8158    0.6910    0.7482      1000\n",
      "     politics     0.7898    0.6800    0.7308      1000\n",
      "       sports     0.8511    0.8460    0.8485      1000\n",
      "         game     0.8555    0.6750    0.7546      1000\n",
      "entertainment     0.5981    0.7470    0.6643      1000\n",
      "\n",
      "     accuracy                         0.7495     10000\n",
      "    macro avg     0.7596    0.7495    0.7510     10000\n",
      " weighted avg     0.7596    0.7495    0.7510     10000\n",
      "\n",
      "电影评论训练集样本数：25000\n",
      "电影评论测试集样本数：25000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "文本预处理: 100%|██████████| 25000/25000 [01:26<00:00, 289.02it/s]\n",
      "文本预处理: 100%|██████████| 25000/25000 [00:54<00:00, 457.72it/s]\n",
      "D:\\Program Files\\JetBrains\\PyCharm 2024.2.1\\test1\\venv\\Lib\\site-packages\\sklearn\\svm\\_base.py:305: ConvergenceWarning: Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------- SVM模型 (最大迭代次数: 500) ----------------\n",
      "训练集准确率: 0.7830\n",
      "测试集准确率: 0.7475\n",
      "训练时间: 43.08 秒\n",
      "分类报告:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "         neg     0.7535    0.7358    0.7445     12500\n",
      "         pos     0.7418    0.7593    0.7505     12500\n",
      "\n",
      "    accuracy                         0.7475     25000\n",
      "   macro avg     0.7477    0.7475    0.7475     25000\n",
      "weighted avg     0.7477    0.7475    0.7475     25000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T06:52:10.961119Z",
     "start_time": "2025-06-06T06:52:10.267500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "\n",
    "# 设置字体以避免警告\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei']  # 指定中文字体为黑体\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "# ------------------------------ 可视化函数 ------------------------------\n",
    "def visualize_results(thucnews_results, imdb_results):\n",
    "    # 混淆矩阵可视化\n",
    "    plt.figure(figsize=(12, 6))\n",
    "\n",
    "    # 新闻标题数据集混淆矩阵\n",
    "    plt.subplot(1, 2, 1)\n",
    "    sns.heatmap(thucnews_results[\"confusion_matrix\"], annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "    plt.title(\"新闻标题数据集混淆矩阵\")\n",
    "    plt.xlabel(\"预测标签\")\n",
    "    plt.ylabel(\"真实标签\")\n",
    "\n",
    "    # 电影评论数据集混淆矩阵\n",
    "    plt.subplot(1, 2, 2)\n",
    "    sns.heatmap(imdb_results[\"confusion_matrix\"], annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "    plt.title(\"电影评论数据集混淆矩阵\") \n",
    "    plt.xlabel(\"预测标签\")\n",
    "    plt.ylabel(\"真实标签\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # 准确率和训练时间柱状图对比\n",
    "    plt.figure(figsize=(10, 5))\n",
    "\n",
    "    # 准确率对比\n",
    "    plt.subplot(1, 2, 1)\n",
    "    datasets = [\"新闻标题数据集\", \"电影评论数据集\"]\n",
    "    accuracies = [thucnews_results[\"test_accuracy\"], imdb_results[\"test_accuracy\"]]\n",
    "    plt.bar(datasets, accuracies, color=[\"skyblue\", \"lightgreen\"])\n",
    "    plt.title(\"数据集准确率对比\")\n",
    "    plt.xlabel(\"数据集\")\n",
    "    plt.ylabel(\"准确率\")\n",
    "    for i, v in enumerate(accuracies):\n",
    "        plt.text(i, v + 0.01, f\"{v:.4f}\", ha=\"center\")\n",
    "\n",
    "    # 训练时间对比\n",
    "    plt.subplot(1, 2, 2)\n",
    "    train_times = [thucnews_results[\"train_time\"], imdb_results[\"train_time\"]]\n",
    "    plt.bar(datasets, train_times, color=[\"skyblue\", \"lightgreen\"])\n",
    "    plt.title(\"数据集训练时间对比\")\n",
    "    plt.xlabel(\"数据集\")\n",
    "    plt.ylabel(\"训练时间 (秒)\")\n",
    "    for i, v in enumerate(train_times):\n",
    "        plt.text(i, v + 0.1, f\"{v:.2f}\", ha=\"center\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 调用可视化函数\n",
    "if __name__ == \"__main__\":\n",
    "    visualize_results(thucnews_results, imdb_results)"
   ],
   "id": "b55e63f4b14ecd5d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 4 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 2
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
